The global energy crisis and environmental crisis have prompted people to seek cleaner, green new energy, and solar energy has drawn considerable attentions among the clean energies by virtue of advantages, such as pollution-free, sustainability, universality, flexibility and reliability. In a case that the photovoltaic system is increasingly put into operation in the power grid, as the very core part in the photovoltaic system, the photovoltaic inverter contributes to safe, stable and efficient operation of the whole system. Compared to the conventional two-level inverter, the three-level inverter is widely applied in the photovoltaic power generation system, because the switching device thereof has the advantages of being connected in series and voltage-sharing, low in switching loss, low in output voltage harmonic content, high in working efficiency and the like. However, since the number of the switching devices is increased in the three-level inverter, the reliability of the circuit is also reduced correspondingly, and fault on any device may cause abnormal operation of the circuit, or even cause a secondary fault, resulting in enormous economic losses.
Fault diagnosis problems of the photovoltaic three-level inverter mainly lie in three aspects: first, in the aspect of the circuit fault mode, the open circuit fault of a single device is only taken into account, multiple-fault mode diagnosis that multiple devices fail simultaneously has been discussed only until recent years, however, the research in this aspect is still less, the problem analysis is still not comprehensive, and the existing methods for diagnosing a fault that the two switching devices are open-circuited simultaneously all have relatively complicated algorithm structures; second, detection signals mostly are an output voltage and an output current, since there are inductive loads on the output end, the current changes slowly, and such always can increase the fault diagnosis time; third, in the diagnosis algorithm aspect, an intelligent diagnosis algorithm is gradually applied to the fields of the inverter fault diagnosis, such as artificial neural networks, support vector machines and extreme learning machines. Among others, neural networks are used more, but neural networks have more defects in nature, such as more parameters to be configured, slow convergence rate and tending to trap into local optimum, which seriously hinders application of the neural networks.
There are many switching devices for the photovoltaic three-level inverter, the types of fault problems are complicated, and the real-time requirement of the system should be satisfied, the conventional method cannot satisfy practical requirements any more. Here, using the data-driven idea, and data generated constantly during operation of the inverter system for reflecting operating mechanism and status of the system, together with appropriate feature extraction and analysis methods, fault diagnosis and recognition of the photovoltaic inverter can be achieved.
Wavelets analysis is a signal time-frequency domain analysis method, and since it can describe time domains and frequency domains of the signal simultaneously and can acquire localized signal information, wavelets analysis has become the focus in fault feature extraction recently. The particle swarm clustering algorithm is obtained by generalizing the particle swarm optimization algorithm. As a swarm intelligence-based emerging evolutionary computing technology, the particle swarm optimization algorithm is swarm intelligence guidance and optimization search generated by cooperating and competing of individuals in the swarm and has a strong universality. The support vector machine is a machine learning algorithm based on a statistical learning theory, has distinct advantages in solving pattern recognition featured by high-dimension, nonlinearity and small-sample, and also has good practical values and wide application prospects in the power electronics fault diagnosis field.